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Bacterial Whole Genome Sequencing as Powerful Tool for Hospital Molecular Epidemiology: Acinetobacter baumannii as a model

Abdalla Ahmed

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia

Microbiology Unit, Department of Laboratory and Blood Bank, King Abdullah Medical City, Makkah, Saudi Arabia

E-mail : aoaahmed@uqu.edu.sa

Bashir Sirag

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia

Fahad Raees

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia

El-Shiekh Kidir

Laboratory Medicine Department, Faculty of Applied Medical Sciences, Umm Al-Qura University, Saudi Arabia

Tayseer Ali

Microbiology Unit, Department of Laboratory and Blood Bank, King Abdullah Medical City, Makkah, Saudi Arabia

Mohammad Atiqur Rahman

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia

Sami Ashgar

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia

Abeer Barhameen

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia

Abdelrahman Elsawy

Medical Microbiology Department, Al-Noor Specialist Hospital, Makkah, Saudi Arabia

Asmaa Mostafa

Medical Microbiology Department, Al-Noor Specialist Hospital, Makkah, Saudi Arabia

Sheerin Shalam

Microbiology Unit, Department of Laboratory and Blood Bank, King Abdullah Medical City, Makkah, Saudi Arabia

DOI: 10.15761/CMID.1000103

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Abstract

In this study, the power of whole genome sequence data in the characterization of 40 clinical isolates of Acinetobacter baumannii was explored. The aim is this study is to demonstrate how bacterial genomic data can be analyzed using easy and semi-automated bioinformatics tools to find answers to  clinical microbiology diagnostic problems. These bioinformatics tools can use assembled or un-assembled genome data for species identification, prediction of antibiotics resistant mechanisms and genotyping.  In the studied sample, genomics data was successfully used to correct species identification and confirm resistant phenotypes. In addition, multi locus sequence types with three novel sequence types were determined.  In conclusion, next generation whole genome sequence data with minor improvement and customization of currently available bioinformatics tools will shortly change the shape of clinical microbiology laboratory services.

Keywords

Acinetobacter baumannii; Molecular epidemiology; Whole Genome Sequencing

Introduction

Recently, the use of Next Generation Sequencing technologies provided unprecedented amount of microbial genomics data. The availability of these genome data is rapidly changing our understanding of microbial behavior, interactions, virulence, antibiotic resistance and genotyping.  With the availability of this sequencing technology, it become so popular and many studies have been published in the last few years reporting the whole genome sequences of clinically important bacterial species such as Klebsiella pneumoniae and Acinetobacter baumannii [1-4]. Whole genome sequence data has been used to study antibiotics resistance [1-3, 5], molecular epidemiology [4,6] and comparative genomics [7,8]. With few exceptions most of these reports were research articles describing bacterial whole genome sequencing with complex data presentation, complex bioinformatics workflow and many bioinformatics terms. These type of research articles are difficult to understand by general readers, such as clinical microbiology practitioners, without special training in bioinformatics.  This knowledge barrier gives wrong impression about the unlimited applications and endless possibilities of using whole genome sequence data in routine clinical microbiology laboratory, which can be directly applied to routine microbiology laboratory. However, a good number of research articles has also been recently published describing a powerful, user-friendly and publicly accessible web-tools with direct applications in clinical microbiology laboratory [9-12].  Only basic knowledge of bioinformatics is needed for the run of these tools and for the interpretation of the generated reports. These tools are extremely useful in microbial characterization and genotyping, and with more minor customization it will become part of the routine microbiology workflow [13].

In this study, we describe original whole genome data used for the study of the molecular epidemiology of Acinetobacter baumannii in tertiary referral hospital in Saudi Arabia. DNA sequencing and data analysis were all done in clinical microbiology departments with no special bioinformatics trained staff. The sequencing results and data analysis will be presented as simple as possible and no complex terms will be mentioned. The aim of this study is to encourage microbiologist to start using these rapidly evolving tools for uncovering the fascinating world of microbial genomics. 

Materials and Methods

Acinetobacter baumannii isolates

During an apparent outbreak of multidrug resistant A. baumannii during 2013, 40 clinical isolates from two hospitals in Makkah, Saudi Arabia, were studied using next generation whole genome sequencing. A. baumannii clinical isolates were obtained from both medical and surgical wards including different intensive care units at King Abdullah Medical City and Al-Noor Specialized Hospital in Makkah, Saudi Arabia. Identification and susceptibility testing in the two hospitals were done routinely using Siemens MicroScan® WalkAway®-96 Plus System (Siemens, Germany). Clinical isolates were stored at -20°C in 10% glycerol peptone water. DNA sequencing and data analysis were done in the Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.

DNA extraction and genome sequencing

Bacterial cells from fresh cultures were used for DNA extraction. Cells were harvested from overnight cultures and washed using sterile Tris EDTA buffer (TE) pH 8.0 in 2 mL screw cap tubes and then re-suspended in 500 µl TE buffer. The cell wall was disrupted using 0.1 mm glass beads in BioSpec Mini-Beadbeater-16 (BioSpec Inc., USA) for 5 minutes then cooled in ice for additional 5 minutes. Aqueous layer containing DNA was separated from proteins and cell debris using two Phenol/Chloroform (1:24 pH 8.0) extractions. DNA was then precipitated by iso-propanol, washed by 70% ethanol, dried at room temperature and re-suspended into 35 µl TE buffer pH 8.0. The quantity and quality of the isolated DNA was determined using Qubit® (Invitrogen, Applied Bio systems, USA), and Agilent Bio analyzer 2100 using 1000 DNA Chip (Agilent Inc., USA).

Library preparation for DNA sequencing

A. baumannii DNA libraries for whole genome  sequencing were prepared using Illumina NexteraXT Library Preparation Kit and samples were barcoded using NexteraXT Index Kit (Illumina Inc., USA). DNA sequencing libraries were prepared using 1 ng input genomic DNA, and  validated and quantified directly without normalization using Agilent Bio analyzer 2100 High Sensitivity DNA Chip (Agilent Inc., USA).  A. baumannii genomes were sequenced in Illumina MiSeq using pair ends protocol and version-3 600 cycles kit. The quality of the pair ends sequence reads were checked by FastQC before sequence assembly (BaseSpace Labs, Illumine Inc., USA).

Genome assembly

De novo assembly of A. baumannii genomes were done using DNASTAR SeqMan NGen 12.3.1 (DNASTAR, Madison, USA) using default settings, which include terming of low quality sequences ends. 

Species identification and sequence-based typing

Assembled genomes were used for 16s rRNA based species identification and Multi Locus Sequencing Typing (MLST). 16s species identification and MLST were done using SpeciesFinder 1.0 Server and MLST 1.8 Server from the Center for Genomics Epidemiology [10,14].

Predication of antibiotics resistance mechanisms

In this study, antibiotics resistance mechanisms were predicted using multiple available tools.  The antibiotics resistant genes were predicted using ResFinder Server from the Center for Genomics Epidemiology  and SRST2 (BaseSpace Labs, Illumine Inc., USA) [11,15]. Antibiotics resistant genes, in selected strains of A. baumannii, were also predicted using Resistance Gene Identifier (RGI), which is designed and developed by the laboratories of Drs. Gerry Wright and Andrew G. McArthur of McMaster University [16,17]. The RGI provides a preliminary annotation of DNA or protein sequence(s), based upon the data available in the Comprehensive Antibiotic Resistance Database (CARD). With all above mentioned tools, only genes with >80% template coverage and minimum match percentage of 90% were reported.

Results

MicroScan® WalkAway®-96 Plus System was able to identify all isolates as A. baumannii. The antimicrobial susceptibility testing showed resistance of all isolates to meropenem and imipenem. Half of the isolates were resistant to colistin with (minimum inhibitory concentration of equal to or higher than 4 μg/ml.  

Thirty-nine isolates were successfully sequenced and only one isolate failed sequencing.  The summary of the de novo assembly is shown in table 1. Based on the 16s rRNA sequences, 36 isolates were correctly identified as A. baumannii using SpeciesFinder tool [14]. One isolate was found to be Stenotrophomonas maltophilia and another isolate was identified as Acinetobacter species. One of the isolates was identified with low confidence as Serratia marcescens (only 33% of reads were aligned to species level). The three non-A. baumannii isolates were excluded from analysis in this study.

Table 1: Summary of assembly statistics of whole genome sequencing of 36 clinical isolates of A. baumannii. All isolates were sequenced in the same MiSeq sequencing run using pair-end library with 600 sequencing cycles. Sequencing coverage were variable, but sequence data were enough for isolates characterization. Genome assembly was done using DNASTAR SeqMan NGen 12.3.1 (DNASTAR, Madison, USA).

Strain ID

Assembled Sequences

Contigs number

Contigs >2K

Contig N50

Average Coverage

Average Quality

AB254

539471

123

114

61000

35

35

AB250

1506011

124

65

135000

93

36

AB393

404991

136

117

48000

24

35

AB263

2866840

142

62

122000

180

35

AB578

393169

146

133

52000

23

36

AB466

793272

148

116

64000

52

35

AB252

836036

169

135

47000

49

36

AB487

673304

175

127

60000

42

35

AB601

431553

181

142

57000

27

35

AB559

543683

184

152

44000

32

35

AB469

359745

198

155

39000

21

36

AB596

566539

216

164

41000

35

36

AB354

767629

228

190

37000

51

35

AB309

1400536

244

109

69000

74

36

AB357

373552

249

204

34000

24

35

AB388

676161

286

228

26000

43

36

AB552

349431

293

253

27000

20

36

AB492

539559

319

272

22000

29

36

AB321

273488

321

293

20000

17

35

ABNH8

254274

333

319

19000

16

35

ABNH6

462054

452

411

16000

23

35

AB363

303315

466

427

12000

19

36

AB314

168044

509

484

7000

10

36

AB595

157553

513

500

8000

9

35

AB543

537413

523

362

14000

23

36

AB576

222018

541

489

8000

14

35

AB217

552147

551

435

12000

29

36

AB558

269568

575

501

9000

17

35

AB432

404823

693

496

9000

24

36

ABNH1

261285

695

602

6000

16

35

ABNH2

294471

766

627

6000

18

35

ABNH7

292329

770

636

6000

18

35

AB417

267777

775

634

6000

16

36

AB462

863503

782

494

8000

43

36

ABNH4

292141

809

661

5000

18

35

ABNH3

218238

816

718

4000

13

35

Average

558776

401

329

32222

33

35

Maximum

2866840

816

718

135000

180

36

Minimum

157553

123

62

4000

9

35

Six known sequence types were identified (ST195, ST218, ST208, ST281, ST557 and ST884). ST195 was the most common sequence type accounting for 47% of all A. baumannii isolates (17/36). Three novel sequence types were found among seven isolates collected from both hospitals (Table 2). These novel sequence types were submitted to the A. baumannii MLST database (http://pubmlst.org/ abaumannii/) [18]. The new novel sequence types were designated as ST1286, ST1287 and ST1288 (Table 2).  ST218 was found in more than half (4/7) of A. baumannii isolates from  AL-Noor Hospital, in which two of the remaining isolates had two different novel sequence types  and the third one was found to be ST195, which was the major prevalent sequence types as described above. No clear correlations were noticed between sequence types and certain wards, specimens, or infection date. No correlation was identified between sequence types and biotypes as determined by the MicroScan® WalkAway®-96 Plus System (Table 2).

Table 2: Clinical data, antibiotics susceptibility results, biotypes and multi locus sequences types of 36 clinical isolates of A. baumannii. Antibiotics susceptibility and biotypes were determined using MicroScan® WalkAway®-96 Plus System. Sequence types were predicted using whole genome assembled contigs using the MLST server from the Center for Genomics Epidemiology.

Isolate Number

Site

Ward

Date

Colistin

Imipenem Susceptibility

Meropenem Susceptibility

Biotype

Sequence type genes alleles

Sequence Type

cpn60

gdhb

glta

gpi

gyrb

reca

rpod

AB217

Wound swab

ER

Oct 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_97

gyrb_3

reca_2

rpod_3

ST-208

AB250

Sputum

ICU

Oct 2012

Resistant

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_97

gyrb_3

reca_2

rpod_3

ST-208

AB252

sputum

ICU

Oct 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB254

Wound swab

Surgical

Nov 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB263

sputum

ICU

Nov 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB309

Blood

ER

Jan 2013

Resistant

Resistant

Resistant

60770

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB314

Blood

ICU

Jan 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB321

Wound Swab

ICU

Jan 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB354

Sputum

ICU

Feb 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB357

Rectal Swab

ICU

Feb 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_99

gyrb_17

reca_2

rpod_3

ST-281

AB363

Rectal Swab

ICU

Feb 2013

Susceptible

Resistant

Resistant

62730

cpn60_1

gdhb_2

glta_18

gpi_83

gyrb_87

reca_28

rpod_71

ST-884

AB388

Rectal Swab

CCU

Feb 2013

Resistant

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB393

Sputum

CCU

Feb 2013

Susceptible

Resistant

Resistant

66730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB417

Blood

HEAM

Feb 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB432

Rectal Swab

CCU

Mar 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_66

gyrb_38

reca_2

rpod_3

ST-557

AB462

Blood

SICU

Mar 2013

Resistant

Resistant

Resistant

62743

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB466

Rectal Swab

SICU

Mar 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_66

gyrb_38

reca_2

rpod_3

ST-557

AB469

Urine

LTCU

Apr 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB487

Urine

CCU

Apr 2013

Resistant

Resistant

Resistant

2062770

cpn60_2

gdhb_3

glta_1

gpi_97

gyrb_3

reca_2

rpod_3

ST-208

AB492

Blood

CCU

Apr 2013

Susceptible

Resistant

Resistant

24620

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB508

Blood

ICU

May 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB543

Blood

ICU

Jul 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB552

Blood

LTCU

Jul 2013

Susceptible

Resistant

Resistant

2062620

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB558

Sputum

ICU

Jul 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB559

Rectal Swab

ICU

Jul 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_99

gyrb_17

reca_2

rpod_3

ST-281

AB576

Rectal Swab

ICU

Aug 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB578

Blood

ER

Aug 2013

Susceptible

Resistant

Resistant

66624

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB595

Rectal Swab

ICU

Sep 2013

Resistant

Resistant

Resistant

62760

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB596

Urine

ER

Sep 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB601

Rectal Swab

ICU

Sep 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

ABNH1

Blood

ICU

 Apr 2014

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH2

Tissue

Surgical

 Apr 2014

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH3

Tissue

Surgical

 Apr 2014

Resistant

Resistant

Resistant

62730

cpn60_2

gdhb_115

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

Novel 1 (ST1287)

ABNH4

Wound swab

Surgical

 Apr 2014

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH6

Wound swab

Surgical

 Apr 2014

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH7

Wound swab

Surgical

 Apr 2014

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_117

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

Novel 1 (ST1288)

ABNH8

Wound swab

Surgical

 Apr 2014

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

Bioinformatics tools predicted the presence of large number of resistant genes known to confer resistant to a wide range of major antibiotics classes. Resistant genes to Aminoglycoside, Beta-lactam, Fluoroquinolone, Macrolide, Lincosamide, Phenicol, Sulphonamide and Tetracycline were detected in most of the isolates (Table 3 and 4). OXA-51-like carbapenemases (OXA-66) was found in all isolates, followed by OAX-23, which was found in more than 90% of the isolates. OXA-40-like (OXA-72) was found in only 5 strains. No other Carbapenem-hydrolyzing OXA-type, NDM-type, VIM-type, or IMP-type carbapenemases were detected in our study population (Table 3 and 4).

Table 3: Antibiotics resistant genes in Acinetobacter baumanniias. SRST2 version 1.0.0 (Illumina BaseSpace) was used to predict the resistant genes using ARG-ANNOT database. Only genes detected with >90% coverage are reported.

Antibiotics Resistant Genes

Strain ID

aac6-Ib

Aac3

AacA4

aada1

AadA2

aph3

AphA6

armA

BlaA1

BlaA2

CatB8

Mb1

MphE

MsrE

OXA-23

OXA-66

OXA-72

StrA

StrB

Sul1

Sul2

TEM-ID

TetB

Zn dependent hydrolase

bla class C

AB217

ND

ND

ND

ND

ND

D

D

ND

ND

D

ND

D

ND

ND

D

D

ND

D

D

ND

ND

D

D

D

D

AB252

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB254

D

ND

ND

D

ND

D

D

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB263

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB309

D

D

ND

ND

ND

ND

ND

ND

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB314

D

D

ND

D

ND

D

D

D

ND

D

D

D

D

D

ND

D

ND

D

D

D

ND

ND

D

D

D

AB321

ND

ND

D

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB354

ND

ND

D

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB363

ND

ND

ND

ND

ND

ND

D

ND

ND

ND

ND

D

ND

ND

D

D

ND

D

D

D

D

ND

ND

D

D

AB388

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB393

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB417

D

D

ND

ND

ND

ND

ND

ND

ND

D

ND

D

D

D

ND

D

D

D

D

D

D

ND

D

D

D

AB432

ND

ND

ND

ND

ND

ND

D

ND

ND

D

ND

D

D

D

D

D

ND

D

D

D

D

ND

D

D

D

AB462

D

D

ND

ND

ND

ND

ND

ND

D

D

ND

D

D

D

ND

D

D

D

D

D

D

ND

D

D

D

AB466

ND

D

ND

ND

ND

ND

D

ND

ND

D

ND

D

D

D

D

D

D

D

D

D

D

ND

D

D

D

AB469

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

AB487

ND

ND

ND

ND

ND

ND

D

ND

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

ND

D

D

D

AB492

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB543

ND

ND

ND

ND

ND

D

ND

ND

ND

D

ND

D

ND

ND

D

D

ND

D

D

ND

ND

D

D

D

D

AB552

D

D

ND

ND

ND

ND

ND

ND

ND

D

ND

D

D

D

D

D

D

D

D

ND

D

ND

D

D

D

AB558

D

D

ND

ND

ND

ND

ND

ND

ND

D

ND

D

D

D

D

D

D

D

D

D

D

ND

D

D

D

AB559

ND

D

ND

D

ND

D

ND

ND

ND

D

ND

D

ND

ND

D

D

ND

D

D

D

D

D

D

D

D

AB576

D

ND

ND

D

ND

D

D

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB578

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB595

D

ND

ND

ND

ND

D

ND

D

ND

ND

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

AB596

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB601

ND

ND

D

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

ABNH1

ND

ND

ND

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

ND

D

D

D

D

D

D

D

ND

ABNH2

ND

ND

ND

ND

ND

D

D

D

ND

ND

ND

D

D

D

D

D

ND

D

D

D

D

D

D

D

ND

ABNH3

ND

ND

ND

ND

ND

D

D

D

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

ND

ABNH4

ND

ND

ND

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

ABNH6

ND

ND

ND

ND

ND

D

D

D

ND

ND

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

ABNH7

ND

ND

ND

ND

ND

ND

ND

D

ND

ND

ND

D

D

D

D

D

ND

D

D

ND

ND

ND

D

D

ND

ABNH8

ND

ND

ND

ND

ND

ND

ND

D

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

ND

D

D

D

     

D

Resistant Gene Detected

ND

Resistant Gene not detected

                                                                 

Table 4: Prediction of antibiotics resistant genes in A. baumannii strains AB250 and AB508 using Resistance Gene Identifier, which is designed and developed by the laboratories of Drs. Gerry Wright and Andrew G. McArthur of McMaster University.

Resistant Gene

A. baumanniias strains

Antibiotic Resistant Ontology Category

AB250

AB508

aac(6')-ib9

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

aada

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

abem

D

D

efflux pump conferring antibiotic resistance

abes

D

D

efflux pump conferring antibiotic resistance

acrf

D

D

efflux pump conferring antibiotic resistance; beta-lactam resistance gene; fluoroquinolone resistance gene

adea

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene

adef

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; fluoroquinolone resistance gene

adeh

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; fluoroquinolone resistance gene

adei

D

D

chloramphenicol resistance gene; lincosamide resistance gene; macrolide resistance gene; fluoroquinolone resistance gene; efflux pump conferring antibiotic resistance; aminocoumarin resistance gene; tetracycline resistance gene; rifampin resistance gene; beta-lactam resistance gene; trimethoprim resistance gene

adek

D

D

chloramphenicol resistance gene; lincosamide resistance gene; macrolide resistance gene; fluoroquinolone resistance gene; efflux pump conferring antibiotic resistance; aminocoumarin resistance gene; tetracycline resistance gene; rifampin resistance gene; beta-lactam resistance gene; trimethoprim resistance gene

aden

D

D

chloramphenicol resistance gene; gene modulating antibiotic efflux; lincosamide resistance gene; macrolide resistance gene; fluoroquinolone resistance gene; efflux pump conferring antibiotic resistance; aminocoumarin resistance gene; tetracycline resistance gene; rifampin resistance gene; beta-lactam resistance gene; trimethoprim resistance gene

ader

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; gene modulating antibiotic efflux

ades

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; gene modulating antibiotic efflux

aph(3')-ia

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

aph(3'')-ib

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

aph(6)-id

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

arma

D

D

antibiotic target modifying enzyme; aminoglycoside resistance gene

catb8

D

D

chloramphenicol resistance gene; antibiotic inactivation enzyme

mext

D

D

efflux pump conferring antibiotic resistance; chloramphenicol resistance gene; trimethoprim resistance gene; gene modulating antibiotic efflux; fluoroquinolone resistance gene

msre

D

D

efflux pump conferring antibiotic resistance; streptogramin resistance gene; macrolide resistance gene

oxa-23

D

D

antibiotic inactivation enzyme; beta-lactam resistance gene

oxa-66

D

D

antibiotic inactivation enzyme; beta-lactam resistance gene

sul1

D

D

antibiotic target replacement protein; sulfonamide resistance gene

tem-1

D

ND

antibiotic inactivation enzyme; beta-lactam resistance gene

teta

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene

     

D

Resistant Gene Detected

ND

Resistant Gene not detected

                       

Discussion

Good results are always obtained when comparing genomic data with phenotypic results generated by commercially available microbiology automated identification and antimicrobial susceptibility testing systems such as Microscan or Vitek2.  These systems provide acceptable and reliable data of species identification and antibiotics susceptibility profiling for routine clinical setting and offer excellent assistance in patient  management. However, in case of atypical strains or in case of hospital outbreaks, these phenotypic data usually has limited resolution required for understanding complex resistant phenotypes, outbreak clones, colonization dynamics and species identity of poorly differentiated organisms. In routine hospital outbreak investigation, many molecular biology techniques are used to understand the genetic basis of resistant to a single antibiotic and/or to determine the genotype(s) responsible for the outbreak. These molecular tools include DNA sequencing of tens and hundreds of target genes to search for resistant and typing markers to resolve the resistant mechanisms and to determine the genotype of each outbreak isolate. 

In the presence of currently available next generation technology, huge sequence data become available for each clinical isolates, which can provide immediate microbiology diagnostic solutions. In the current study, we demonstrate power of currently available bioinformatics tools that are capable of analyzing whole genome sequence data and provide total clinically relevant data within acceptable short time frame that can influence patient care. Species identification and antimicrobial susceptibility testing, which are the most important and routine duty of the clinical microbiology laboratory, can be determined directly as soon as the bacterial genomic data become available from the DNA sequencing platform [11,15]. However, there is still need for better bioinformatics tools that directly handle DNA sequence data and perform a sequence of automated bioinformatics workflow followed by automated data interpretation tools to generate easily understandable clinical reports.

Whole genome sequence data can also be used to answer questions beyond the routine clinical microbiology daily needs. This is typically useful in case of outbreak investigations, when genetics relation between different clinical isolates from the same species need to be determined. In this study, some tools have been used to study the clonal relationships between relatively large number of clinical isolates of A. baumannii from two closely related hospitals. Using draft genome sequence, therefore assembled contigs, multi locus sequence types were determined using web based free tool from the Center for Genomics Epidemiology [10,14]. Similar tool, SRST2, is also available from the Illumina BaseSpace, which is an application that reports the presence of sequences types and/or reference genes from a database of sequences for virulence genes, resistance genes, and plasmid replicons.

In this study, 40 clinical isolates were multiplexed in one MiSeq sequencing run using version 3 pair-end library with 600 sequencing cycles. Only one samples failed sequencing, but the remaining samples produced sequence data enough for full isolates characterization (Table 1). Larger number of bacterial genomes can be studied in one batch using sequencing platforms with higher data output such as NextSeq and HiSeq. Therefore, for big clinical microbiology larger sample size can still be sequences and genomics reports can be generated within the same time-frame. In routine microbiology, no single test can be used to produce comparable data with similar power. In this study, only species, antibiotics resistant genes and sequence types were determined. However, using the same genome sequence data many other features can be studied using many freely available and user-friendly tools. The advancement of sequencing technology foster the development of several tools for immediate virulence genes detections, plasmids profiling, serotypes predictions and much more [19-21].

The presence of multiple sequence types in our A. baumannii isolates indicate that apparent resistant outbreak was not caused by a single clone. The most prevalent sequence types was ST195 accounting for 47% of all A. baumannii isolates. Similar results were recently reported from the same region [22,23]. ST195 and ST557 were reported by Alyamani and his group from isolates collected form the same city [22], while ST195 and ST208 were reported by study done by Zowawi et al. [23] in isolates representing the Arabian Gulf region [23]. ST195, ST208 and ST218 were found to be closely related to each other with only difference in one allele.  ST195 in A. baumannii was also reported from many other different regions such as India, China and Malaysia [24-26]. In addition to the known sequence types, three novel sequence types were found among seven isolates collected from both hospitals (Table 2). Zowawi et al. [23] also reported three novel sequence types in the Arabian Gulf region study [23]. In this study, these novels sequence types were curated and assigned to new sequence types (ST1286, ST1287 and ST1288) Oxford scheme at the A. baumannii MLST database.

Using genome sequence data, a wide range of antimicrobial resistance genes to major antibiotics classes were predicted, which were in consistent with the phenotypic data obtained by Microscan.  Different carbapenem resistant genes were reported in different studies in our region [22,23,27-31]. However, most of these studies used PCR based detection, which need careful design to insure coverage of all carbapenems resistant genes. In addition, by using PCR-based detection of resistant markers it is difficult to use the term “molecular characterization” for even a single class of antibiotics. Therefore, the power of whole genome sequence remains unbeatable in the screening of all acquired and naturally occurring resistant mechanisms not only for carbapenems, but also for all resistant mechanisms to all known antibiotics classes.  In our study, large number of resistant mechanisms were identified (Tables 3,4 and 5). Only whole genome sequence data was used for the prediction of these antibiotics resistant mechanisms. Many user friendly bioinformatics tools were tested for prediction of antibiotics resistant mechanisms, which were nicely consistent with each other (Tables 4 and 5). One of these tools is the Illumia BaseSapce SRST2, which can provide clinically relevant antibiotics resistant data within acceptable time frame that can influence patient care.

Table 5: Prediction of antibiotics resistant genes in A. baumannii strains AB250 and AB508 using ResFinder form Center for Genomics Epidemiology. Only gene with minimum of 90% similarity and 80% template coverage are reported.

Resistance gene

AB508

AB250

Phenotype

Identity %

Accession no.

Identity %

Accession no.

aac(6')Ib-cr

99.23

EF636461

99.23

EF636461

Fluoroquinolone and aminoglycoside resistance

aacA4

99.64

KM278199

99.64

KM278199

Aminoglycoside resistance

aadA1

100

JQ414041

100

JQ414041

Aminoglycoside resistance

aph(3')-Ic

100

X62115

99.88

X62115

Aminoglycoside resistance

armA

100

AY220558

100

AY220558

Aminoglycoside resistance

blaADC-25

99.8

EF016355

99.91

EF016355

Beta-lactam resistance

blaOXA-23

100

HQ700358

100

HQ700358

Beta-lactam resistance

blaOXA-66

100

FJ360530

100

FJ360530

Beta-lactam resistance

blaTEM-1D

Not detected

100

AF188200

Beta-lactam resistance

catB8

100

AF227506

100

AF227506

Phenicol resistance

mph(E)

100

EU294228

100

EU294228

Macrolide resistance

msr(E)

100

EU294228

100

EU294228

Macrolide, Lincosamide and Streptogramin B resistance

strA

100

M96392

100

M96392

Aminoglycoside resistance

strB

100

M96392

100

M96392

Aminoglycoside resistance

sul1

100

AY224185

100

CP002151

Sulphonamide resistance

tet(B)

100

AP000342

100

AP000342

Tetracycline resistance

In classical molecular hospital epidemiology, antibiogram data with genotyping results are usually combined to trace infections source and to understand colonization patterns in patients, healthcare workers and hospital environment. However, when whole genome sequence data become available from clinical isolates, better hospital molecular epidemiology data with high resolution will help in identifying complex outbreak dynamics and evolution. With genome data, unlimited features can be studied and proper microbial molecular characterization can be achieved.

In conclusion, next generation sequencing data is transforming clinical microbiology routine services. In near future, genomics based characterization will replace number of currently used microbiology techniques such as routine bacterial identification, susceptibility testing and serotyping. Microbial genotyping, which normally carried out in case of hospital outbreaks investigation or as part of research projects, will be part of routine the clinical microbiology reports.  In near future, new hospital patients admission will routinely be screened for all known antibiotics resistant mechanisms instead off only being screened for carbapenems or methicillin resistance. 

Acknowledgement

This work was supported by grant number 12-BIO2319-10 from King Abdul-Aziz City Science and Technology, Riyadh, Saudi Arabia.

References

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Editorial Information

Editor-in-Chief

Nigel Silman
University of the West of England

Article Type

Research Article

Publication history

Received: May 26, 2016
Accepted: June 29, 2016
Published: July 01, 2016

Copyright

©2016 Abdalla Ahmed. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Citation

Abdalla Ahmed, Bashir Sirag, Fahad Raees, El-Shiekh Kidir, Tayseer Ali (2016) Bacterial whole genome sequencing as powerful tool for hospital molecular epidemiology: Acinetobacter baumannii as a model. Clin Microbiol Infect Dis, 1: DOI: 10.15761/CMID.1000103

Corresponding author

Abdalla Ahmed

Department of Microbiology, College of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia, Tel: +966543031577.

E-mail : aoaahmed@uqu.edu.sa

Table 1: Summary of assembly statistics of whole genome sequencing of 36 clinical isolates of A. baumannii. All isolates were sequenced in the same MiSeq sequencing run using pair-end library with 600 sequencing cycles. Sequencing coverage were variable, but sequence data were enough for isolates characterization. Genome assembly was done using DNASTAR SeqMan NGen 12.3.1 (DNASTAR, Madison, USA).

Strain ID

Assembled Sequences

Contigs number

Contigs >2K

Contig N50

Average Coverage

Average Quality

AB254

539471

123

114

61000

35

35

AB250

1506011

124

65

135000

93

36

AB393

404991

136

117

48000

24

35

AB263

2866840

142

62

122000

180

35

AB578

393169

146

133

52000

23

36

AB466

793272

148

116

64000

52

35

AB252

836036

169

135

47000

49

36

AB487

673304

175

127

60000

42

35

AB601

431553

181

142

57000

27

35

AB559

543683

184

152

44000

32

35

AB469

359745

198

155

39000

21

36

AB596

566539

216

164

41000

35

36

AB354

767629

228

190

37000

51

35

AB309

1400536

244

109

69000

74

36

AB357

373552

249

204

34000

24

35

AB388

676161

286

228

26000

43

36

AB552

349431

293

253

27000

20

36

AB492

539559

319

272

22000

29

36

AB321

273488

321

293

20000

17

35

ABNH8

254274

333

319

19000

16

35

ABNH6

462054

452

411

16000

23

35

AB363

303315

466

427

12000

19

36

AB314

168044

509

484

7000

10

36

AB595

157553

513

500

8000

9

35

AB543

537413

523

362

14000

23

36

AB576

222018

541

489

8000

14

35

AB217

552147

551

435

12000

29

36

AB558

269568

575

501

9000

17

35

AB432

404823

693

496

9000

24

36

ABNH1

261285

695

602

6000

16

35

ABNH2

294471

766

627

6000

18

35

ABNH7

292329

770

636

6000

18

35

AB417

267777

775

634

6000

16

36

AB462

863503

782

494

8000

43

36

ABNH4

292141

809

661

5000

18

35

ABNH3

218238

816

718

4000

13

35

Average

558776

401

329

32222

33

35

Maximum

2866840

816

718

135000

180

36

Minimum

157553

123

62

4000

9

35

Table 2: Clinical data, antibiotics susceptibility results, biotypes and multi locus sequences types of 36 clinical isolates of A. baumannii. Antibiotics susceptibility and biotypes were determined using MicroScan® WalkAway®-96 Plus System. Sequence types were predicted using whole genome assembled contigs using the MLST server from the Center for Genomics Epidemiology.

Isolate Number

Site

Ward

Date

Colistin

Imipenem Susceptibility

Meropenem Susceptibility

Biotype

Sequence type genes alleles

Sequence Type

cpn60

gdhb

glta

gpi

gyrb

reca

rpod

AB217

Wound swab

ER

Oct 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_97

gyrb_3

reca_2

rpod_3

ST-208

AB250

Sputum

ICU

Oct 2012

Resistant

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_97

gyrb_3

reca_2

rpod_3

ST-208

AB252

sputum

ICU

Oct 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB254

Wound swab

Surgical

Nov 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB263

sputum

ICU

Nov 2012

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB309

Blood

ER

Jan 2013

Resistant

Resistant

Resistant

60770

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB314

Blood

ICU

Jan 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB321

Wound Swab

ICU

Jan 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB354

Sputum

ICU

Feb 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB357

Rectal Swab

ICU

Feb 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_99

gyrb_17

reca_2

rpod_3

ST-281

AB363

Rectal Swab

ICU

Feb 2013

Susceptible

Resistant

Resistant

62730

cpn60_1

gdhb_2

glta_18

gpi_83

gyrb_87

reca_28

rpod_71

ST-884

AB388

Rectal Swab

CCU

Feb 2013

Resistant

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB393

Sputum

CCU

Feb 2013

Susceptible

Resistant

Resistant

66730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB417

Blood

HEAM

Feb 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB432

Rectal Swab

CCU

Mar 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_66

gyrb_38

reca_2

rpod_3

ST-557

AB462

Blood

SICU

Mar 2013

Resistant

Resistant

Resistant

62743

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB466

Rectal Swab

SICU

Mar 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_66

gyrb_38

reca_2

rpod_3

ST-557

AB469

Urine

LTCU

Apr 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB487

Urine

CCU

Apr 2013

Resistant

Resistant

Resistant

2062770

cpn60_2

gdhb_3

glta_1

gpi_97

gyrb_3

reca_2

rpod_3

ST-208

AB492

Blood

CCU

Apr 2013

Susceptible

Resistant

Resistant

24620

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB508

Blood

ICU

May 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB543

Blood

ICU

Jul 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB552

Blood

LTCU

Jul 2013

Susceptible

Resistant

Resistant

2062620

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB558

Sputum

ICU

Jul 2013

Susceptible

Resistant

Resistant

62720

cpn60_2

gdhb_3

glta_1

gpi_100

gyrb_35

reca_2

rpod_3

Novel 1 (ST1286)

AB559

Rectal Swab

ICU

Jul 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_99

gyrb_17

reca_2

rpod_3

ST-281

AB576

Rectal Swab

ICU

Aug 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB578

Blood

ER

Aug 2013

Susceptible

Resistant

Resistant

66624

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB595

Rectal Swab

ICU

Sep 2013

Resistant

Resistant

Resistant

62760

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB596

Urine

ER

Sep 2013

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

AB601

Rectal Swab

ICU

Sep 2013

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

ABNH1

Blood

ICU

 Apr 2014

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH2

Tissue

Surgical

 Apr 2014

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH3

Tissue

Surgical

 Apr 2014

Resistant

Resistant

Resistant

62730

cpn60_2

gdhb_115

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

Novel 1 (ST1287)

ABNH4

Wound swab

Surgical

 Apr 2014

Resistant

Resistant

Resistant

62770

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH6

Wound swab

Surgical

 Apr 2014

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_102

gyrb_3

reca_2

rpod_3

ST-218

ABNH7

Wound swab

Surgical

 Apr 2014

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_117

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

Novel 1 (ST1288)

ABNH8

Wound swab

Surgical

 Apr 2014

Susceptible

Resistant

Resistant

62730

cpn60_2

gdhb_3

glta_1

gpi_96

gyrb_3

reca_2

rpod_3

ST-195

Table 3: Antibiotics resistant genes in Acinetobacter baumanniias. SRST2 version 1.0.0 (Illumina BaseSpace) was used to predict the resistant genes using ARG-ANNOT database. Only genes detected with >90% coverage are reported.

Antibiotics Resistant Genes

Strain ID

aac6-Ib

Aac3

AacA4

aada1

AadA2

aph3

AphA6

armA

BlaA1

BlaA2

CatB8

Mb1

MphE

MsrE

OXA-23

OXA-66

OXA-72

StrA

StrB

Sul1

Sul2

TEM-ID

TetB

Zn dependent hydrolase

bla class C

AB217

ND

ND

ND

ND

ND

D

D

ND

ND

D

ND

D

ND

ND

D

D

ND

D

D

ND

ND

D

D

D

D

AB252

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB254

D

ND

ND

D

ND

D

D

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB263

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB309

D

D

ND

ND

ND

ND

ND

ND

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB314

D

D

ND

D

ND

D

D

D

ND

D

D

D

D

D

ND

D

ND

D

D

D

ND

ND

D

D

D

AB321

ND

ND

D

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB354

ND

ND

D

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB363

ND

ND

ND

ND

ND

ND

D

ND

ND

ND

ND

D

ND

ND

D

D

ND

D

D

D

D

ND

ND

D

D

AB388

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB393

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB417

D

D

ND

ND

ND

ND

ND

ND

ND

D

ND

D

D

D

ND

D

D

D

D

D

D

ND

D

D

D

AB432

ND

ND

ND

ND

ND

ND

D

ND

ND

D

ND

D

D

D

D

D

ND

D

D

D

D

ND

D

D

D

AB462

D

D

ND

ND

ND

ND

ND

ND

D

D

ND

D

D

D

ND

D

D

D

D

D

D

ND

D

D

D

AB466

ND

D

ND

ND

ND

ND

D

ND

ND

D

ND

D

D

D

D

D

D

D

D

D

D

ND

D

D

D

AB469

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

AB487

ND

ND

ND

ND

ND

ND

D

ND

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

ND

D

D

D

AB492

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB543

ND

ND

ND

ND

ND

D

ND

ND

ND

D

ND

D

ND

ND

D

D

ND

D

D

ND

ND

D

D

D

D

AB552

D

D

ND

ND

ND

ND

ND

ND

ND

D

ND

D

D

D

D

D

D

D

D

ND

D

ND

D

D

D

AB558

D

D

ND

ND

ND

ND

ND

ND

ND

D

ND

D

D

D

D

D

D

D

D

D

D

ND

D

D

D

AB559

ND

D

ND

D

ND

D

ND

ND

ND

D

ND

D

ND

ND

D

D

ND

D

D

D

D

D

D

D

D

AB576

D

ND

ND

D

ND

D

D

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB578

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB595

D

ND

ND

ND

ND

D

ND

D

ND

ND

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

AB596

D

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

AB601

ND

ND

D

D

ND

D

ND

D

ND

D

D

D

D

D

D

D

ND

D

D

D

ND

D

D

D

D

ABNH1

ND

ND

ND

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

ND

D

D

D

D

D

D

D

ND

ABNH2

ND

ND

ND

ND

ND

D

D

D

ND

ND

ND

D

D

D

D

D

ND

D

D

D

D

D

D

D

ND

ABNH3

ND

ND

ND

ND

ND

D

D

D

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

ND

ABNH4

ND

ND

ND

ND

ND

D

ND

D

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

ABNH6

ND

ND

ND

ND

ND

D

D

D

ND

ND

ND

D

D

D

D

D

ND

D

D

ND

ND

D

D

D

D

ABNH7

ND

ND

ND

ND

ND

ND

ND

D

ND

ND

ND

D

D

D

D

D

ND

D

D

ND

ND

ND

D

D

ND

ABNH8

ND

ND

ND

ND

ND

ND

ND

D

ND

D

ND

D

D

D

D

D

ND

D

D

ND

ND

ND

D

D

D

     

D

Resistant Gene Detected

ND

Resistant Gene not detected

                                                                 

Table 4: Prediction of antibiotics resistant genes in A. baumannii strains AB250 and AB508 using Resistance Gene Identifier, which is designed and developed by the laboratories of Drs. Gerry Wright and Andrew G. McArthur of McMaster University.

Resistant Gene

A. baumanniias strains

Antibiotic Resistant Ontology Category

AB250

AB508

aac(6')-ib9

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

aada

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

abem

D

D

efflux pump conferring antibiotic resistance

abes

D

D

efflux pump conferring antibiotic resistance

acrf

D

D

efflux pump conferring antibiotic resistance; beta-lactam resistance gene; fluoroquinolone resistance gene

adea

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene

adef

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; fluoroquinolone resistance gene

adeh

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; fluoroquinolone resistance gene

adei

D

D

chloramphenicol resistance gene; lincosamide resistance gene; macrolide resistance gene; fluoroquinolone resistance gene; efflux pump conferring antibiotic resistance; aminocoumarin resistance gene; tetracycline resistance gene; rifampin resistance gene; beta-lactam resistance gene; trimethoprim resistance gene

adek

D

D

chloramphenicol resistance gene; lincosamide resistance gene; macrolide resistance gene; fluoroquinolone resistance gene; efflux pump conferring antibiotic resistance; aminocoumarin resistance gene; tetracycline resistance gene; rifampin resistance gene; beta-lactam resistance gene; trimethoprim resistance gene

aden

D

D

chloramphenicol resistance gene; gene modulating antibiotic efflux; lincosamide resistance gene; macrolide resistance gene; fluoroquinolone resistance gene; efflux pump conferring antibiotic resistance; aminocoumarin resistance gene; tetracycline resistance gene; rifampin resistance gene; beta-lactam resistance gene; trimethoprim resistance gene

ader

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; gene modulating antibiotic efflux

ades

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene; gene modulating antibiotic efflux

aph(3')-ia

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

aph(3'')-ib

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

aph(6)-id

D

D

antibiotic inactivation enzyme; aminoglycoside resistance gene

arma

D

D

antibiotic target modifying enzyme; aminoglycoside resistance gene

catb8

D

D

chloramphenicol resistance gene; antibiotic inactivation enzyme

mext

D

D

efflux pump conferring antibiotic resistance; chloramphenicol resistance gene; trimethoprim resistance gene; gene modulating antibiotic efflux; fluoroquinolone resistance gene

msre

D

D

efflux pump conferring antibiotic resistance; streptogramin resistance gene; macrolide resistance gene

oxa-23

D

D

antibiotic inactivation enzyme; beta-lactam resistance gene

oxa-66

D

D

antibiotic inactivation enzyme; beta-lactam resistance gene

sul1

D

D

antibiotic target replacement protein; sulfonamide resistance gene

tem-1

D

ND

antibiotic inactivation enzyme; beta-lactam resistance gene

teta

D

D

efflux pump conferring antibiotic resistance; tetracycline resistance gene

     

D

Resistant Gene Detected

ND

Resistant Gene not detected

                       

Table 5: Prediction of antibiotics resistant genes in A. baumannii strains AB250 and AB508 using ResFinder form Center for Genomics Epidemiology. Only gene with minimum of 90% similarity and 80% template coverage are reported.

Resistance gene

AB508

AB250

Phenotype

Identity %

Accession no.

Identity %

Accession no.

aac(6')Ib-cr

99.23

EF636461

99.23

EF636461

Fluoroquinolone and aminoglycoside resistance

aacA4

99.64

KM278199

99.64

KM278199

Aminoglycoside resistance

aadA1

100

JQ414041

100

JQ414041

Aminoglycoside resistance

aph(3')-Ic

100

X62115

99.88

X62115

Aminoglycoside resistance

armA

100

AY220558

100

AY220558

Aminoglycoside resistance

blaADC-25

99.8

EF016355

99.91

EF016355

Beta-lactam resistance

blaOXA-23

100

HQ700358

100

HQ700358

Beta-lactam resistance

blaOXA-66

100

FJ360530

100

FJ360530

Beta-lactam resistance

blaTEM-1D

Not detected

100

AF188200

Beta-lactam resistance

catB8

100

AF227506

100

AF227506

Phenicol resistance

mph(E)

100

EU294228

100

EU294228

Macrolide resistance

msr(E)

100

EU294228

100

EU294228

Macrolide, Lincosamide and Streptogramin B resistance

strA

100

M96392

100

M96392

Aminoglycoside resistance

strB

100

M96392

100

M96392

Aminoglycoside resistance

sul1

100

AY224185

100

CP002151

Sulphonamide resistance

tet(B)

100

AP000342

100

AP000342

Tetracycline resistance

Figure 1. Lay-out of the Dutch mobile laboratories deployed to West Africa, 2015. (A). Lay-out of the 20 feet container laboratory as deployed to Sinje, Liberia and Freetown, Sierra Leone. (B). Lay-out of the 40 feet container laboratory as deployed to Kono, Sierra Leone.  (copy right: Hospitainer Apeldoorn, the Netherlands).

Figure 2. Schematic presentation of sample workflow and time-line of Dutch Mobile laboratories in West Africa, 2015 for ~ 10 samples. Step 1: Sample arrival: check biosafe containment and administration (5 min.). Step 2: Clinical forms photocopied for further administration and decontamination with 0.5% hypochlorite (15 min.). Step 3: Decontamination inner package in 1% hypochlorite, inactivation sample for EBOV testing,  and Malaria testing (if whole blood) in Biosafety level 3 cabinet (75 min.). Decontamination sample tubes for EBOV testing for export out of Biosafety cabinet.  Step 4: Automated nucleic acid extraction  (60 min. including set-up of the machine). Step 5: Ebola virus real-time RT-PCR (70 min).  Step 6: Interpretation PCR data including Quality Assurance check, administration and result reporting to responsible clinician (not shown, 30 min.).

Figure 3. Location and catchment area Dutch Mobile Laboratories in West Africa, 2015. (A) Overview location and total spectrum of Ebola virus laboratories in Sierra Leone and Liberia as reported by WHO in February 2015. Dutch laboratories are represented by #13 (Sinje, Liberia) and # 20 (Kono, Sierra Leone) and #21 (Freetown, Sierra Leone). Source: WHO situation report 25 March 2015 (http://apps.who.int/ebola/current-situation/ebola-situation-report-25-march-2015). (B) Overview of catchment area two Dutch mobile laboratories in Sierra Leone based on village of residence of patients of which samples were tested.  Residence data Sinje, Liberia were uninterpretable.

Figure 4. Statistics of daily sample numbers Dutch Mobile Laboratories, West Africa, 2015. (A) The combined number of specimens tested for each laboratory per day. (B) The number of specimens tested per day by EBOV test result for the laboratories combined. (C) The number of specimens tested per day by Malaria test result for the three laboratories combined.

Figure 5. Specimen type received at Dutch Mobile laboratories in Sierra Leone, 2015. (A) Kono, and (B) Freetown.